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CT Perfusion Map Synthesis from CTP Dynamic Images Using a Learned LSTM Generative Adversarial Network for Acute Ischemic Stroke Assessment

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Abstract

Computed tomography perfusion (CTP) is a dynamic 4-dimensional imaging technique (3-dimensional volumes captured over approximately 1 min) in which cerebral blood flow is quantified by tracking the passage of a bolus of intravenous contrast with serial imaging of the brain. To diagnose and assess acute ischemic stroke, the standard method relies on summarizing acquired CTPs over the time axis to create maps that show different hemodynamic parameters, such as the timing of the bolus arrival and passage (Tmax and MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV). However, producing accurate CTP maps requires the selection of an arterial input function (AIF), i.e. a time-concentration curve in one of the large feeding arteries of the brain, which is a highly error-prone procedure. Moreover, during approximately one minute of CT scanning, the brain is exposed to ionizing radiation that can alter tissue composition, and create free radicals that increase the risk of cancer. This paper proposes a novel end-to-end deep neural network that synthesizes CTP images to generate CTP maps using a learned LSTM Generative Adversarial Network (LSTM-GAN). Our proposed method can improve the precision and generalizability of CTP map extraction by eliminating the error-prone and expert-dependent AIF selection step. Further, our LSTM-GAN does not require the entire CTP time series and can produce CTP maps with a reduced number of time points. By reducing the scanning sequence from about 40 to 9 time points, the proposed method has the potential to minimize scanning time thereby reducing patient exposure to CT radiation. Our evaluations using the ISLES 2018 challenge dataset consisting of 63 patients showed that our model can generate CTP maps by using only 9 snapshots, without AIF selection, with an accuracy of \(84.37\%\).

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Data Availability

The dataset used in this work is available online and can be downloaded from the following link: http://www.isles-challenge.org/ISLES2018/.

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Funding

This work was supported by the Computing Science and Medical Departments of the University of Alberta.

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Author notes

  1. Pierre Boulanger and Brian Buck contributed equally to this work.

Authors and Affiliations

  1. Computing Science Department, University of Alberta, Edmonton, Canada

    Mohsen Soltanpour & Pierre Boulanger

  2. Medicine Deptment, University of Alberta, Edmonton, Canada

    Brian Buck

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  1. Mohsen Soltanpour
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Contributions

Mohsen Soltanpour contributed as the main writer of the manuscript, conducting experiments, and preparing figures and other materials. Brian Buck and Pierre Boulanger provided oversight and supervision throughout the experimental processes and manuscript preparation.

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Correspondence to Mohsen Soltanpour.

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Soltanpour, M., Boulanger, P. & Buck, B. CT Perfusion Map Synthesis from CTP Dynamic Images Using a Learned LSTM Generative Adversarial Network for Acute Ischemic Stroke Assessment. J Med Syst 48, 37 (2024). https://doi.org/10.1007/s10916-024-02054-2

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